Micromobility Management Model in last-mile Urban Logistics
Anna A. Likhacheva, Master's student. Perm Branch of the National Research University
Higher School of Economics
Alexander I. Deryabin, Associate Professor of Information Technology in Business Department.
National Research University «Higher School of Economics»
Abstract
Low-dose computed tomography reduces lung cancer mortality, but its widespread implementation is limited by a high rate of false-positive results. To improve screening efficiency, predictive models that maintain a specified level of specificity (typically ≥ 90 %) are required. This study compared the sensitivity of the established PLCOm2012 model with machine learning methods (XGBoost, neural network) under a fixed specificity constraint using synthetic data simulating population-level lung cancer risk. The results demonstrated that the neural network achieved the highest sensitivity (69.4 %), outperforming PLCOm2012 (61.1 %) and enabling the detection of 8.3 % more lung cancer cases without increasing the number of false-positive predictions. Thus, ML models show promise for enhancing screening efficacy by capturing complex patterns in the data. Their application could improve lung cancer detection rates without increasing the burden on the healthcare system.
Keywords: micromobility; smart city; urban logistics; last-mile delivery; microdepot; multi-criteria optimization; intelligent transport system; machine learning.
For citation: Likhacheva A. A., Deryabin A. I. Micromobility Management Model in last-mile Urban Logistics. Digital Models and Solutions. 2026. Vol. 5, no. 2, pp. 19–35. DOI: 10.29141/ 2949-477X-2026-5-2-2. EDN: GEFELY.
